Mlabonne

<div align="center"> <p align="center"> 𝕏 <a href="https://twitter.com/maximelabonne">Follow me on X</a> • 🤗 <a href="https://huggingface.co/mlabonne">Hugging Face</a> • 💻 <a href="https://mlabonne.github.io/blog">Blog</a> • 📙 <a href="https://github.com/PacktPublishing/LLM-Engineers-Handbook">LLM Engineer's Handbook</a> </p> </div> <br/>

Hi, I'm a Machine Learning Scientist, Author, Blogger, and LLM Developer.

💼 Projects

• The LLM Course: A popular curated list of resources to get into LLMs (>70k ⭐).
• LLM Datasets: Curated list of high-quality datasets for LLM fine-tuning.
• LLM Tools: Automate LLM pipelines with Colab notebooks like LLM AutoEval, LazyMergekit, LazyAxolotl, and AutoQuant.

🤗 Models

• LFM2: My work at Liquid AI is to post-train our own pre-trained LLMs with a custom architecture. [Playground]
• Abliterated models: Collection of abliterated models to remove refusals. [Article]
• Fine-tunes & Merges: Popular models like NeuralDaredevil-8B, AlphaMonarch-7B, NeuralBeagle14-7B, or NeuralHermes (first successful open-source DPO) and MoEs like Phixtral (first Phi-based MoE) and Beyonder-4x7B-v3.

📚 Books

• LLM Engineer's Handbook: Practical guide about LLM engineering with fine-tuning, RAG, data, evaluation, deployment, and more!
• Hands-on GNNs: Technical book on how to design and implement many types of graph neural networks for various use cases.